Independent Risk Factors of Catheter-Related Thrombosis (CRT) in Adult Cancer Patients: An Individual Patient-Level Data (IPD) Meta-Analysis of Randomized Clinical Trials and Prospective Cohort Studies
Bibliographic record
Abstract
Abstract Purpose: Symptomatic catheter-related thrombosis (CRT) complicates roughly 5% of central venous catheter insertions in cancer patients. Recent studies have failed to show a reduction in CRT with routine use of pharmacologic thromboprophylaxis in all patients. We conducted a meta-analysis of individual patient-level data to identify independent, baseline risk factors of CRT that may help to select high-risk patients for prophylaxis. Methods: PubMed, EMBASE, CINAHL, CENTRAL, DARE, Grey literature databases were searched in all languages from 1995–2008. Only prospective studies and randomized controlled trials (RCTs) were included. The primary end-point was objectively confirmed symptomatic and asymptomatic CRT. Multivariate logistic regression analysis of 17 prespecified baseline characteristics was conducted. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were estimated. Results: A total sample of 5636 subjects from 5 RCTs and 7 prospective studies was included in the analysis. Among these subjects, 425 CRT events were observed (8%). In multivariate logistic regression, the use of implanted ports as compared with peripherally implanted central venous catheters (PICC), decreased CRT risk (OR = 0.43; 95% CI, 0.23–0.80). Alternatively, past history of deep vein thrombosis (DVT) (OR = 2.03; 95% CI, 1.05–3.92), subclavian venipuncture insertion technique (OR = 2.16; 95% CI, 1.07–4.34), and improper catheter tip location (OR = 1.92; 95% CI, 1.22–3.02), increased CRT risk. Conclusions: CRT risk is increased with using PICC catheters, previous history of DVT, subclavian venipuncture insertion technique and improper positioning of the catheter tip. These factors may be useful for risk stratifying patients to select those for thromboprophylaxis. Prospective studies are needed to validate these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.049 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".